Brain Age Prediction: Deep Models Need a Hand to Generalize.

ABSTRACT: Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns about robust and accurate generalization in new data limit their clinical applicability. The large number of trainable parameters, combined with limited medical imaging training data, contributes to this challenge, often resulting in a generalization gap where there is a significant discrepancy between model performance on training data versus unseen data. In this study, we assess a deep model, SFCN-reg, based on the VGG-16 architecture, and address the generalization gap through comprehensive preprocessing, extensive data augmentation, and model r

SUBMITTER: Rajabli R 

PROVIDER: S-EPMC12265028 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature